OpenRaiser/Intern-Atlas huggingface dataset:
Sense-Giving Strategies of Media Organisations in Social Media Disaster Communication: Findings from Hurricane Harvey
Venue: arXiv cs.SI 2020
Year: 2020

Media organisations are essential communication stakeholders in social media disaster communication during extreme events. They perform gatekeeper and amplification roles which are crucial for collective sense-making processes. In that capacity, media organisations distribute information through social media, use it as a source of information, and share such information across different channels. Yet, little is known about the role of media organisations on social media as supposed sense-givers to effectively support the creation of mutual sense. This study investigates the communication strategies of media organisations in extreme events. A Twitter dataset consisting of 9,414,463 postings was collected during Hurricane Harvey in 2017. Social network and content analysis methods were applied to identify media communication approaches. Three different sense-giving strategies could be identified: retweeting of local in-house outlets; bound amplification of messages of individual to the organisation associated journalists; and open message amplification.
You are now an Influencer! Measuring CEO Reputation in Social Media
Venue: arXiv cs.SI 2020
Year: 2020

We know that reputation in organisational contexts can be understood as a valuable asset that requires diligent management. It directly affects how a firm is publicly perceived, and indirectly, how a firm will perform economically. The establishment of social media as ubiquitous tools of communication have changed how corporations manage their reputation. Particularly CEOs face novel responsibilities, as they deal with their personal image, which at the same time affects the reputation of their firm. Whereas CEO and corporate reputation have been researched isolated from each other, little is known about how a CEO's social media reputation management affects corporate reputation. This research in progress paper aims to emphasise this research gap with a literature review on the current status of reputation management and measurement by means of social media. We further propose a research design that combines sentiment analysis, frequency detection, and content analysis and discuss further research prospects.
Modeling Survival in model-based Reinforcement Learning
Venue: arXiv cs.LG 2020
Year: 2020

Although recent model-free reinforcement learning algorithms have been shown to be capable of mastering complicated decision-making tasks, the sample complexity of these methods has remained a hurdle to utilizing them in many real-world applications. In this regard, model-based reinforcement learning proposes some remedies. Yet, inherently, model-based methods are more computationally expensive and susceptible to sub-optimality. One reason is that model-generated data are always less accurate than real data, and this often leads to inaccurate transition and reward function models. With the aim to mitigate this problem, this work presents the notion of survival by discussing cases in which the agent's goal is to survive and its analogy to maximizing the expected rewards. To that end, a substitute model for the reward function approximator is introduced that learns to avoid terminal states rather than to maximize accumulated rewards from safe states. Focusing on terminal states, as a small fraction of state-space, reduces the training effort drastically. Next, a model-based reinforcement learning method is proposed (Survive) to train an agent to avoid dangerous states through a safety map model built upon temporal credit assignment in the vicinity of terminal states. Finally, the performance of the presented algorithm is investigated, along with a comparison between the proposed and current methods.
Stochastic Weighted Matching: $(1-ε)$ Approximation
Venue: arXiv cs.DS 2020
Year: 2020

Let $G=(V, E)$ be a given edge-weighted graph and let its {\em realization} $\mathcal{G}$ be a random subgraph of $G$ that includes each edge $e \in E$ independently with probability $p$. In the {\em stochastic matching} problem, the goal is to pick a sparse subgraph $Q$ of $G$ without knowing the realization $\mathcal{G}$, such that the maximum weight matching among the realized edges of $Q$ (i.e. graph $Q \cap \mathcal{G}$) in expectation approximates the maximum weight matching of the whole realization $\mathcal{G}$. In this paper, we prove that for any desirably small $ε\in (0, 1)$, every graph $G$ has a subgraph $Q$ that guarantees a $(1-ε)$-approximation and has maximum degree only $O_{ε, p}(1)$. That is, the maximum degree of $Q$ depends only on $ε$ and $p$ (both of which are known to be necessary) and not for example on the number of nodes in $G$, the edge-weights, etc. The stochastic matching problem has been studied extensively on both weighted and unweighted graphs. Previously, only existence of (close to) half-approximate subgraphs was known for weighted graphs [Yamaguchi and Maehara, SODA'18; Behnezhad et al., SODA'19]. Our result substantially improves over these works, matches the state-of-the-art for unweighted graphs [Behnezhad et al., STOC'20], and essentially settles the approximation factor.
Graph Neural Network based Channel Tracking for Massive MIMO Networks
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we resort to the graph neural network (GNN) and propose the new channel tracking method for the massive multiple-input multiple-output networks under the high mobility scenario. We first utilize a small number of pilots to achieve the initial channel estimation. Then, we represent the obtained channel data in the form of graphs and describe the channel spatial correlation by the weights along the edges of the graph. Furthermore, we introduce the computation steps of the main unit for the GNN and design a GNN-based channel tracking framework, which includes an encoder, a core network and a decoder. Simulation results corroborate that our proposed GNN-based scheme can achieve better performance than the works with feedforward neural network.
Resource Allocation in Co-existing Optical Wireless HetNets
Venue: arXiv cs.NI 2020
Year: 2020

In multi-user optical wireless communication (OWC) systems interference between users and cells can significantly affect the quality of OWC links. Thus, in this paper, a mixed-integer linear programming (MILP) model is developed to establish the optimum resource allocation in wavelength division multiple access (WDMA) optical wireless systems. Consideration is given to the optimum allocation of wavelengths and access points (APs) to each user to support multiple users in an environment where Micro, Pico and Atto Cells co-exist for downlink communication. The high directionality of light rays in small cells, such as Pico and Atto cells, can offer a very high signal to noise and interference ratio (SINR) at high data rates. Consideration is given in this work to visible light communication links which utilise four wavelengths per access point (red, green, yellow and blue) for Pico and Atto cells systems, while the Micro cell system uses an infrared (IR) transmitter. Two 10-user scenarios are considered in this work. All users in both scenarios achieve a high optical channel bandwidth beyond 7.8 GHz. In addition, all users in the two scenarios achieve high SINR beyond the threshold (15.6 dB) needed for 10-9 on off keying (OOK) bit error rate at a data rate of 7.1 Gbps.
An end-to-end CNN framework for polarimetric vision tasks based on polarization-parameter-constructing network
Venue: arXiv cs.CV 2020
Year: 2020

Pixel-wise operations between polarimetric images are important for processing polarization information. For the lack of such operations, the polarization information cannot be fully utilized in convolutional neural network(CNN). In this paper, a novel end-to-end CNN framework for polarization vision tasks is proposed, which enables the networks to take full advantage of polarimetric images. The framework consists of two sub-networks: a polarization-parameter-constructing network (PPCN) and a task network. PPCN implements pixel-wise operations between images in the CNN form with 1x1 convolution kernels. It takes raw polarimetric images as input, and outputs polarization-parametric images to task network so as to complete a vison task. By training together, the PPCN can learn to provide the most suitable polarization-parametric images for the task network and the dataset. Taking faster R-CNN as task network, the experimental results show that compared with existing methods, the proposed framework achieves much higher mean-average-precision (mAP) in object detection task
TriGAN: Image-to-Image Translation for Multi-Source Domain Adaptation
Venue: arXiv cs.CV 2020
Year: 2020

Most domain adaptation methods consider the problem of transferring knowledge to the target domain from a single source dataset. However, in practical applications, we typically have access to multiple sources. In this paper we propose the first approach for Multi-Source Domain Adaptation (MSDA) based on Generative Adversarial Networks. Our method is inspired by the observation that the appearance of a given image depends on three factors: the domain, the style (characterized in terms of low-level features variations) and the content. For this reason we propose to project the image features onto a space where only the dependence from the content is kept, and then re-project this invariant representation onto the pixel space using the target domain and style. In this way, new labeled images can be generated which are used to train a final target classifier. We test our approach using common MSDA benchmarks, showing that it outperforms state-of-the-art methods.
Pattern Learning for Detecting Defect Reports and Improvement Requests in App Reviews
Venue: arXiv cs.CL 2020
Year: 2020

Online reviews are an important source of feedback for understanding customers. In this study, we follow novel approaches that target this absence of actionable insights by classifying reviews as defect reports and requests for improvement. Unlike traditional classification methods based on expert rules, we reduce the manual labour by employing a supervised system that is capable of learning lexico-semantic patterns through genetic programming. Additionally, we experiment with a distantly-supervised SVM that makes use of noisy labels generated by patterns. Using a real-world dataset of app reviews, we show that the automatically learned patterns outperform the manually created ones, to be generated. Also the distantly-supervised SVM models are not far behind the pattern-based solutions, showing the usefulness of this approach when the amount of annotated data is limited.
On the rules of science game
Venue: arXiv cs.DL 2020
Year: 2020

Credit allocation in the mainstream bibliometrics is fundamentally flawed and the popular indicators have been misleading science for decades. Originally a simple technical mistake has become an integral part of our culture and is very difficult to correct. Although the problem has been raised in scientific articles, it seems mostly unknown to wider audience.
A Chinese Corpus for Fine-grained Entity Typing
Venue: arXiv cs.CL 2020
Year: 2020

Fine-grained entity typing is a challenging task with wide applications. However, most existing datasets for this task are in English. In this paper, we introduce a corpus for Chinese fine-grained entity typing that contains 4,800 mentions manually labeled through crowdsourcing. Each mention is annotated with free-form entity types. To make our dataset useful in more possible scenarios, we also categorize all the fine-grained types into 10 general types. Finally, we conduct experiments with some neural models whose structures are typical in fine-grained entity typing and show how well they perform on our dataset. We also show the possibility of improving Chinese fine-grained entity typing through cross-lingual transfer learning.
Faster Algorithms for Quantitative Analysis of Markov Chains and Markov Decision Processes with Small Treewidth
Venue: arXiv cs.DS 2020
Year: 2020

Discrete-time Markov Chains (MCs) and Markov Decision Processes (MDPs) are two standard formalisms in system analysis. Their main associated quantitative objectives are hitting probabilities, discounted sum, and mean payoff. Although there are many techniques for computing these objectives in general MCs/MDPs, they have not been thoroughly studied in terms of parameterized algorithms, particularly when treewidth is used as the parameter. This is in sharp contrast to qualitative objectives for MCs, MDPs and graph games, for which treewidth-based algorithms yield significant complexity improvements. In this work, we show that treewidth can also be used to obtain faster algorithms for the quantitative problems. For an MC with $n$ states and $m$ transitions, we show that each of the classical quantitative objectives can be computed in $O((n+m)\cdot t^2)$ time, given a tree decomposition of the MC that has width $t$. Our results also imply a bound of $O(κ\cdot (n+m)\cdot t^2)$ for each objective on MDPs, where $κ$ is the number of strategy-iteration refinements required for the given input and objective. Finally, we make an experimental evaluation of our new algorithms on low-treewidth MCs and MDPs obtained from the DaCapo benchmark suite. Our experimental results show that on MCs and MDPs with small treewidth, our algorithms outperform existing well-established methods by one or more orders of magnitude.
Improving Robot Dual-System Motor Learning with Intrinsically Motivated Meta-Control and Latent-Space Experience Imagination
Venue: arXiv cs.LG 2020
Year: 2020

Combining model-based and model-free learning systems has been shown to improve the sample efficiency of learning to perform complex robotic tasks. However, dual-system approaches fail to consider the reliability of the learned model when it is applied to make multiple-step predictions, resulting in a compounding of prediction errors and performance degradation. In this paper, we present a novel dual-system motor learning approach where a meta-controller arbitrates online between model-based and model-free decisions based on an estimate of the local reliability of the learned model. The reliability estimate is used in computing an intrinsic feedback signal, encouraging actions that lead to data that improves the model. Our approach also integrates arbitration with imagination where a learned latent-space model generates imagined experiences, based on its local reliability, to be used as additional training data. We evaluate our approach against baseline and state-of-the-art methods on learning vision-based robotic grasping in simulation and real world. The results show that our approach outperforms the compared methods and learns near-optimal grasping policies in dense- and sparse-reward environments.
Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning
Venue: arXiv cs.CL 2020
Year: 2020

Knowledge graph-based dialogue systems are capable of generating more informative responses and can implement sophisticated reasoning mechanisms. However, these models do not take into account the sparseness and incompleteness of knowledge graph (KG)and current dialogue models cannot be applied to dynamic KG. This paper proposes a dynamic Knowledge graph-based dialogue generation method with improved adversarial Meta-Learning (KDAD). KDAD formulates dynamic knowledge triples as a problem of adversarial attack and incorporates the objective of quickly adapting to dynamic knowledge-aware dialogue generation. We train a knowledge graph-based dialog model with improved ADML using minimal training samples. The model can initialize the parameters and adapt to previous unseen knowledge so that training can be quickly completed based on only a few knowledge triples. We show that our model significantly outperforms other baselines. We evaluate and demonstrate that our method adapts extremely fast and well to dynamic knowledge graph-based dialogue generation.
Approximate Nearest Neighbour Search on Privacy-aware Encoding of User Locations to Identify Susceptible Infections in Simulated Epidemics
Venue: arXiv cs.IR 2020
Year: 2020

Amidst an increasing number of infected cases during the Covid-19 pandemic, it is essential to trace, as early as possible, the susceptible people who might have been infected by the disease due to their close proximity with people who were tested positive for the virus. This early contact tracing is likely to limit the rate of spread of the infection within a locality. In this paper, we investigate how effectively and efficiently can such a list of susceptible people be found given a list of infected persons and their locations. To address this problem from an information retrieval (search) perspective, we represent the location of each person at each time instant as a point in a vector space. By using the locations of the given list of infected persons as queries, we investigate the feasibility of applying approximate nearest neighbour (ANN) based indexing and retrieval approaches to obtain a list of top-k suspected users in real-time. Since leveraging information from true user location data can lead to security and privacy concerns, we also investigate what effects does distance-preserving encoding methods have on the effectiveness of the ANN methods. Experiments conducted on real and synthetic datasets demonstrate that the top-k retrieved lists of susceptible users retrieved with existing ANN approaches (KD-tree and HNSW) yield satisfactory precision and recall values, thus indicating that ANN approaches can potentially be applied in practice to facilitate real-time contact tracing even under the presence of imposed privacy constraints.
Local Differential Privacy based Federated Learning for Internet of Things
Venue: arXiv cs.CR 2020
Year: 2020

Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a large variety of crowdsourcing applications such as Waze, Uber, and Amazon Mechanical Turk, etc. Users of these applications report the real-time traffic information to the cloud server which trains a machine learning model based on traffic information reported by users for intelligent traffic management. However, crowdsourcing application owners can easily infer users' location information, which raises severe location privacy concerns of the users. In addition, as the number of vehicles increases, the frequent communication between vehicles and the cloud server incurs unexpected amount of communication cost. To avoid the privacy threat and reduce the communication cost, in this paper, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model. Specifically, we propose four LDP mechanisms to perturb gradients generated by vehicles. The Three-Outputs mechanism is proposed which introduces three different output possibilities to deliver a high accuracy when the privacy budget is small. The output possibilities of Three-Outputs can be encoded with two bits to reduce the communication cost. Besides, to maximize the performance when the privacy budget is large, an optimal piecewise mechanism (PM-OPT) is proposed. We further propose a suboptimal mechanism (PM-SUB) with a simple formula and comparable utility to PM-OPT. Then, we build a novel hybrid mechanism by combining Three-Outputs and PM-SUB.
Fast-Forward Relaying Scheme to Mitigate Jamming Attacks by Full-Duplex Radios
Venue: arXiv cs.IT 2020
Year: 2020

In this work, we address reliable communication of low-latency packets in the presence of a full-duplex adversary that is capable of executing a jamming attack while also being able to measure the power levels on various frequency bands. Due to the presence of a strong adversary, first, we point out that traditional frequency-hopping does not help since unused frequency bands may not be available, and moreover, the victim's transition between the frequency bands would be detected by the full-duplex adversary. Identifying these challenges, we propose a new cooperative mitigation strategy, referred to as the Semi-Coherent Fast-Forward Full-Duplex (SC-FFFD) relaying technique, wherein the victim node, upon switching to a new frequency band, seeks the assistance of its incumbent user, which is also a full-duplex radio, to instantaneously forward its messages to the destination using a portion of their powers. Meanwhile, the two nodes cooperatively use their residual powers on the jammed frequency band so as to engage the adversary to continue executing the jamming attack on the same band. Using on-off keying (OOK) and phase-shift-keying (PSK) as the modulation schemes at the victim and the helper node, respectively, we derive upper bounds on the probability of error of jointly decoding the information symbols of the two nodes, and subsequently derive analytical solutions to arrive at the power-splitting factor between the two frequency bands to minimize the error of both the nodes. We also present extensive simulation results for various signal-to-noise-ratio values and PSK constellations to showcase the efficacy of the proposed approach.
The Cost of Training NLP Models: A Concise Overview
Venue: arXiv cs.CL 2020
Year: 2020

We review the cost of training large-scale language models, and the drivers of these costs. The intended audience includes engineers and scientists budgeting their model-training experiments, as well as non-practitioners trying to make sense of the economics of modern-day Natural Language Processing (NLP).
DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction
Venue: arXiv cs.LG 2020
Year: 2020

Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scientists entering the biomedical field and bioinformaticians with limited DL experience. We present DeepPurpose, a comprehensive and easy-to-use deep learning library for DTI prediction. DeepPurpose supports training of customized DTI prediction models by implementing 15 compound and protein encoders and over 50 neural architectures, along with providing many other useful features. We demonstrate state-of-the-art performance of DeepPurpose on several benchmark datasets.
The implications of digitalization on business model change
Venue: arXiv cs.SE 2020
Year: 2020

Context: Digitalization brings new opportunities and also challenges to software companies. Objective: Software companies have mostly focused on the technical aspects of handing changes and mostly ignoring the business model changes and their implications on software organization and the architecture. In this paper, we synthesize implications of the digitalization based on an extensive literature survey and a longitudinal case study at Ericsson AB. Method: Using thematic analysis, we present six propositions to be used to facilitate the cross-disciplinary analysis of business model dynamics and the effectiveness and efficiency of the outcome of business modeling, by linking value, transaction, and organizational learning to business model change. Conclusions: Business model alignment is highlighted as a new business model research area for understanding the relationships between the dynamic nature of business models, organization design, and the value creation in the business model activities.